code: def _calculate_metrics(
      self, data: Dict[str, Dict[str, torch.Tensor]]
  ) -> _METRICS:
      return norm_metrics(
          p=1, data=data, target_spaces=self._target_spaces
      )
  
imports: from __future__ import annotations
  from collections import defaultdict
  import functools
  import logging
  from typing import Callable, Dict, List, Literal, Tuple, overload
  import torch
  from .tools import _METRICS, _MASKS, norm_metrics, generate_sparsity, is_active_target
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import TargetType
  from ..base.wrapper import ModuleWrapper
  from ..utils.docstring import _EVALUATOR_DOCSTRING
  from ..utils.evaluator import Evaluator, TensorHook
label_desc: Use image node_id 6 for calling the TaylorPruner obj's underlying member method code with example usage: obj._calculate_metrics(data) and returns: norm_metrics